Visual tracking utilizing robust complementary learner and adaptive refiner

In recent years, several methods have been developed to utilize complementary cues for robust visual tracking. However, the robustness of these trackers is still limited in challenging scenarios such as dramatic illumination variation and heavy occlusion, since the corrupted cues are extracted in these cases. In this paper, we propose a visual tracking algorithm by utilizing robust Complementary Learner and adaptive Refiner (CLR). The complementary learners consist of a correlation filter and a statistical color model. To improve the robustness of the complementary learners, we introduce an accurate correlation filter with the proposed multi-channel feature. The devised effective feature is computed on both brightness channel and nonparametric local rank transformed channel at different resolutions of local cells. We further adopt a feature dimension reduction strategy to boost the tracking speed. Moreover, we investigate the problem of heavy occlusion and train an online SVM as a tracking result refiner to deal with this problem. The refiner model updates an incorrect prediction to a reliable position in case of low reliability of the current tracking result, thereby significantly reducing the risk of model drift risk as well as realizing robust tracking. We extensively evaluate the proposed CLR tracker on the 50 sequences of the OTB-2013 benchmark dataset. The experimental results demonstrate that the proposed tracker outperforms other state-of-the-art methods with real-time speed.